Innovative Valorization of Wood Panel Waste into Activated Biochar for Efficient Phenol Adsorption
Bibliographic record
Abstract
Construction and demolition byproducts include substantial amounts of wood panel waste (WPW) that pose environmental challenges. They also create opportunities for sustainable resource recovery. This study investigates the potential of WPW-derived biochar as an efficient adsorbent for phenol removal from aqueous solutions. Biochar was produced via pyrolysis at 450 °C and subsequent activation at 750, 850, and 950 °C. The biochar’s physicochemical properties, including surface area, pore volume, and elemental composition, were characterized using advanced methods, including BET analysis, elemental analysis, and adsorption isotherm analysis. Activated biochar demonstrated up to nine times higher adsorption capacity than raw biochar, with a maximum of 171.9 mg/g at 950 °C under optimal conditions: pH of 6 at 25 °C, initial phenol concentration of 200 mg/L, and biochar dosage of 1 g/L of solution for 48 h. Kinetic and isotherm studies revealed that phenol adsorption followed a pseudo-second-order model and fit the Langmuir isotherm, indicating chemisorption and monolayer adsorption mechanisms. Leaching tests confirmed the biochar’s environmental safety, with heavy metal concentrations well below regulatory limits. Based on these findings, WPW biochar offers a promising, eco-friendly solution for wastewater treatment in line with circular economy and green chemistry principles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".